Feb

24

Now, are you ready for this? I am not sure that I am, but I feel honour bound, having started a discussion last month under this heading about Internet of Things/Internet of Everything (IoT/IoE), to finish it by relating it back to the information marketplace and the media and publishing world. It is easy enough to think of the universal tagging of the world around us as a revolution in logistics, but surely it’s only effect cannot be to speed the Amazon drone ever more rapidly to our door? Or to create a moving map of a battlefield which relates what we are reading about in a book to all of the places being mentioned as we turn the pages? Or create digital catalogues as every book is tagged and can respond by position and availability!

You are right: there must be more to all of this. So let us start where we are now and move forward with the usual improbable claims that you expect to read here. Let’s begin with automated journalism and authorship, which, when I wrote here about the early work of Narrative Science and the Hanley Wood deal, was in its infancy, and then came Automated Insights and the Wordsmith package (automatedinsights.com). Here, it seemed to me, were the first steps in replacing the reporter who quarries the story from the press release with a flow of standardised analytics which could format the story and reproduce it in the journal in question just as if it had been laboriously crafted by Man. End result is a rapid change in the newspaper or magazine cost base (and an extension to life on Earth for the traditional media?).

I no longer think this will be the case. As with the long history of the postponed glories of Artificial Intelligence itself, by the time fully automated journalism arrives, most readers will be machines as well as most writers, in fields as diverse as business news and sports reporting and legal informatics and diagnostic medicine and science research reporting. Machine 2 Me will be rapidly followed by real M2M – Machine to Machine. The question then sharpens crudely: if the reporting and analysis is data driven and machine moderated, will “publishing” be an intermediary role at all? Or will it simply become a data analysis service, directed by the needs of each user organisation and eventually each user? So the idea of holding content and generalizing it for users becomes less relevant, and is replaced by what I am told is called “Actionable Personalization”. In other words, we move rapidly from machine driven journalism to personalised reporting which drives user workflows and produces solutions.

Let’s stumble a little further along this track. In such a deeply automated world, most things that retain a human touch will assume a high value. Because of their rarity, perhaps, or sometimes because of the eccentric ability of the human brain to retain a detail that fails the jigsaw test until it can be fitted into a later picture. We may need few analysts of this type, but their input will have critical value. Indeed, the distinguishing factors in discriminating between suppliers may not be the speed or capacity or power of their machinery, but the value of their retained humans who have the erratic capacity to disrupt the smooth flow of analytical conclusion – retrospectively. Because we must remember that the share price or the research finding or the analytic comparison has been folded into the composite picture and adjustments made long before any human has had time to actually read it.

Is all this just futurizing? Is there any evidence that the world is beginning to identify objects consistently with markers which will enable a genuine convergence of the real and the virtual? I think that the geolocation people can point to just that happening in a number of instances, and not just to speed the path of driverless cars. The so-called BD2K iniatives feature all sort of data-driven development around projects like the Neuroscience Information Framework. Also funded by the U.S. government, the Genbank initiatives and the development of the International Nucleotide Sequence Database Collaboration, point to a willingness to identify objects in ways that combine processes on the lab workbench with the knowledge systems that surround them. As so often, the STM world becomes a harbinger of change, creating another dimension to the ontologies that already exist in biomedicine and the wider life sciences. With the speed of change steadily increasing these things will not be long in leaving the research bench for a wider world.

Some of the AI companies that will make these changes happen are already in movement, as the recent dealings around Sentient (www.sentient.ai) make clear. Others are still pacing the paddock, though new players like Context Relevant (www.contextrelevant.com) and Scaled Inference (https://scaled inference.com) already have investment and valuations which are comparable to Narrative Science. Then look at the small fast growth players – MetaMind, Vicarious, Nara or Kensho – or even Mastodon C in the UK – to see how quickly generation is now lapping generation. For a decade it has been high fashion for leading market players in information marketplaces to set up incubators to grow new market presence. We who have content will build tools, they said. We will invest in value add in the market and be ready for the inevitable commoditization of our content when it occurs. They were very right to take this view, of course, and it is very satisfying to see investments like ReadCube in the Holtzbrinck/Digital Science greenhouse, or figshare in the same place, beginning to accelerate. But if, as we must by now suspect, the next wave to crash on the digital beach is bigger than the last, then some of these incubations will get flooded out before they reach maturity. Perhaps there was no time at which it is more important to have a fixed focus on 6 months ahead and three years. The result will be a cross-eyed generation, but that may be the price for knowing when to disinvest in interim technology that may never have time to flower.